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Whisfusion demonstrates that masked diffusion decoders can match the accuracy of top autoregressive models like Whisper-large-v3 while eliminating latency scaling with transcript length. By training a specialized decoder on frozen audio embeddings and using parallel decoding, the system achieves a significant speedup of 4-7x across English, European, and CJK benchmarks. This work establishes masked diffusion as a Pareto-competitive paradigm for high-throughput multilingual speech recognition.
Whisfusion: Parallel ASR Decoding with Masked Diffusion
arXiv:2508.07048v2 Announce Type: replace-cross Abstract: Autoregressive (AR) encoder-decoder models dominate high-quality multilingual ASR, but their left-to-right decoders make inference latency scale with transcript length. A natural alternative, CTC-style non-autoregressive (NAR) systems avoid this bottleneck but their conditional independence assumption sacrifices transcript-level generative modeling. Masked diffusion language models (e.g., LLaDA, MDLM) offer a competitive NAR text-generation approach. We ask whether such models can bring NAR ASR into the accuracy regime of strong AR ASR systems while removing the left-to-right bottleneck. We propose Whisfusion, which trains a dedicated masked diffusion decoder from scratch on top of frozen Whisper-large-v3 audio embeddings, denoising masked transcripts in just a few steps. We train on ~68k hours of 11-language speech with high-mask specialization to align training with the fully masked starting point of inference, and decode via Parallel Diffusion Decoding. Whisfusion surpasses Whisper-large-v3 on group-average accuracy across English, European, and CJK benchmarks, while running 4-5x faster, additionally surpassing Whisper-turbo in both accuracy and throughput. It reaches accuracy competitive with Canary and Qwen3-ASR while running 3-7x faster. These results establish masked diffusion as a Pareto-competitive non-autoregressive paradigm for high-throughput multilingual transcription. Code and model weights are available at https://github.com/taeyoun811/Whisfusion.
Abstract:Autoregressive (AR) encoder-decoder models dominate high-quality multilingual ASR, but their left-to-right decoders make inference latency scale with transcript length. A natural alternative, CTC-style non-autoregressive (NAR) systems avoid this bottleneck but their conditional independence assumption sacrifices transcript-level generative modeling. Masked diffusion language models (e.g., LLaDA, MDLM) offer a competitive NAR text-generation approach. We ask whether such models can bring NAR ASR into the accuracy regime of strong AR ASR systems while removing the left-to-right bottleneck. We propose Whisfusion, which trains a dedicated masked diffusion decoder from scratch on top of frozen Whisper-large-v3 audio embeddings, denoising masked transcripts in just a few steps. We train on ~68k hours of 11-language speech with high-mask specialization to align training with the fully masked starting point of inference, and decode via Parallel Diffusion Decoding. Whisfusion surpasses Whisper-large-v3 on group-average accuracy across English, European, and CJK benchmarks, while running 4-5x faster, additionally surpassing Whisper-turbo in both accuracy and throughput. It reaches accuracy competitive with Canary and Qwen3-ASR while running 3-7x faster. These results establish masked diffusion as a Pareto-competitive non-autoregressive paradigm for high-throughput multilingual transcription. Code and model weights are available at this https URL.
Sound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
arXiv:2508.07048 [cs.SD]
(or arXiv:2508.07048v2 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2508.07048 Focus to learn more arXiv-issued DOI via DataCite
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